Split over-training for unsupervised purchase intention identification
Recognizing user-expressed intentions in social media can be useful for many applications such as business intelligence, as intentions are intimately linked to potential actions or behaviors. This paper focuses on a binary classification problem: whether a text expresses purchase intention (PI) or n...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
World Academy of Research in Science and Engineering
2020
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| Online Access: | http://eprints.utem.edu.my/id/eprint/24902/2/IJATCSE214932020.PDF http://eprints.utem.edu.my/id/eprint/24902/ http://www.warse.org/IJATCSE/static/pdf/file/ijatcse214932020.pdf |
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| Institution: | Universiti Teknikal Malaysia Melaka |
| Language: | English |
| Summary: | Recognizing user-expressed intentions in social media can be useful for many applications such as business intelligence, as intentions are intimately linked to potential actions or behaviors. This paper focuses on a binary classification problem: whether a text expresses purchase intention (PI) or not (non-PI). In contrast to existing research, which relies on labeled intention corpus or linguistic knowledge, we proposed an unsupervised method called split over-training for the PI identification task. Experiments on PI identification from tweets showed that our approach was effective and promising. The best classifying accuracy of 84.6% and PI F-measure of 70.4% was achieved, which are only 7.7% and 4.9% respectively lower than fully supervised models. This means our unsupervised method may provide reasonable preprocessing for intention corpus labeling or intention knowledge acquisition. |
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